A simple and effective fusion approach for multi-frame optical flow estimation

Zhile Ren; Orazio Gallo; Deqing Sun; Ming Hsuan Yang; Erik B. Sudderth; Jan Kautz

doi:10.1007/978-3-030-11024-6_53

A simple and effective fusion approach for multi-frame optical flow estimation

Zhile Ren, Orazio Gallo, Deqing Sun, Ming Hsuan Yang, Erik B. Sudderth, Jan Kautz

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks.

Original language	English
Title of host publication	Computer Vision – ECCV 2018 Workshops, Proceedings
Editors	Laura Leal-Taixé, Stefan Roth
Publisher	Springer Verlag
Pages	706-710
Number of pages	5
ISBN (Print)	9783030110239
DOIs	https://doi.org/10.1007/978-3-030-11024-6_53
Publication status	Published - 2019
Event	15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 2018 Sep 8 → 2018 Sep 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11134 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th European Conference on Computer Vision, ECCV 2018
Country/Territory	Germany
City	Munich
Period	18/9/8 → 18/9/14

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-11024-6_53

Cite this

Ren, Z., Gallo, O., Sun, D., Yang, M. H., Sudderth, E. B., & Kautz, J. (2019). A simple and effective fusion approach for multi-frame optical flow estimation. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (pp. 706-710). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11134 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_53

Ren, Zhile ; Gallo, Orazio ; Sun, Deqing ; Yang, Ming Hsuan ; Sudderth, Erik B. ; Kautz, Jan. / A simple and effective fusion approach for multi-frame optical flow estimation. Computer Vision – ECCV 2018 Workshops, Proceedings. editor / Laura Leal-Taixé ; Stefan Roth. Springer Verlag, 2019. pp. 706-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Zhile Ren and Orazio Gallo and Deqing Sun and Yang, {Ming Hsuan} and Sudderth, {Erik B.} and Jan Kautz",

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Ren, Z, Gallo, O, Sun, D, Yang, MH, Sudderth, EB & Kautz, J 2019, A simple and effective fusion approach for multi-frame optical flow estimation. in L Leal-Taixé & S Roth (eds), Computer Vision – ECCV 2018 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11134 LNCS, Springer Verlag, pp. 706-710, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 18/9/8. https://doi.org/10.1007/978-3-030-11024-6_53

A simple and effective fusion approach for multi-frame optical flow estimation. / Ren, Zhile; Gallo, Orazio; Sun, Deqing; Yang, Ming Hsuan; Sudderth, Erik B.; Kautz, Jan.

Computer Vision – ECCV 2018 Workshops, Proceedings. ed. / Laura Leal-Taixé; Stefan Roth. Springer Verlag, 2019. p. 706-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11134 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kautz, Jan

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AB - To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks.

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Ren Z, Gallo O, Sun D, Yang MH, Sudderth EB, Kautz J. A simple and effective fusion approach for multi-frame optical flow estimation. In Leal-Taixé L, Roth S, editors, Computer Vision – ECCV 2018 Workshops, Proceedings. Springer Verlag. 2019. p. 706-710. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-11024-6_53

A simple and effective fusion approach for multi-frame optical flow estimation

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